European Minimum Wages Rose 23-87% Since 2020, With Eastern Europe Leading Growth

Growth trajectories and current positioning relative to €1,686 EU average, monthly statutory wages

MakeoverMonday

Data Visualization

R Programming

2025

Analysis of European minimum wage changes 2020-2025 using improved dual-panel visualization. Eastern European countries led growth (Poland: 87%, Czechia: 51%) while Western nations maintain positions above €1,686 EU average.

Author

Steven Ponce

Published

July 23, 2025

Original

The original visualization Low wages across Europe comes from Datawrapper

Makeover

Figure 1: Dual-panel chart showing European minimum wage changes 2020-2025. Left panel: dumbbell chart displaying growth percentages from 23% (Luxembourg) to 87% (Poland), with gray circles marking 2020 starting wages and colored dots showing 2025 endpoints. Right panel: horizontal bar chart showing current deviation from €1,686 EU average, with blue bars for countries above average (Luxembourg highest at +€952) and red bars for countries below average (Czechia lowest at -€860). Eastern European countries exhibit the highest growth rates but remain below the EU average.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
  tidyverse,      # Easily Install and Load the 'Tidyverse'
  ggtext,         # Improved Text Rendering Support for 'ggplot2'
  showtext,       # Using Fonts More Easily in R Graphs
  scales,         # Scale Functions for Visualization
  glue,           # Interpreted String Literals
  patchwork       # The Composer of Plots
  )
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  12,
    height =  10,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#|
eu_wages <- read_csv(
  here::here('data/MakeoverMonday/2025/wages.csv')) |> 
  janitor::clean_names()
```

3. Examine the Data

Show code

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(eu_wages)
skimr::skim(eu_wages)
```

4. Tidy Data

Show code

```{r}
#| label: tidy
#| warning: false

# Custom euro formatting function
format_euro <- function(x) {
  paste0("€", scales::comma(x, accuracy = 1))
}

# Data tidying
eu_wages_long <- eu_wages |>
  mutate(date = dmy(date)) |>
  pivot_longer(
    cols = -date, 
    names_to = "country",
    values_to = "wage"
  ) |>
  mutate(
    country = str_to_title(country),
    year = year(date),
    wage_euros = wage,
    wage_thousands = wage / 1000
  ) |>
  arrange(country, date)

# Calculate additional metrics
eu_wages_summary <- eu_wages_long |>
  group_by(country) |>
  summarise(
    start_wage = first(wage_euros),
    end_wage = last(wage_euros),
    total_change = end_wage - start_wage,
    pct_change = (end_wage - start_wage) / start_wage * 100,
    avg_wage = mean(wage_euros),
    .groups = "drop"
  ) |>
  arrange(desc(end_wage))

# Calculate average 
current_avg <- mean(eu_wages_summary$end_wage)

# Dumbell data
dumbell_data <- eu_wages_summary |>
  mutate(
    country = fct_reorder(country, end_wage),
    above_avg = end_wage > current_avg
  )

# Diverging data
diverging_chart_data <- eu_wages_summary |>
  mutate(
    diff_from_avg = end_wage - current_avg,
    country = fct_reorder(country, diff_from_avg),
    above_avg = diff_from_avg > 0
  )
```

5. Visualization Parameters

Show code

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = list(
  above_avg = "#1f77b4", 
  below_avg = "#d62728", 
  start_point = "#ff7f0e", 
  end_point = "#2ca02c", 
  connection = "#7f7f7f", 
  text_dark = "#2f2f2f", 
  text_light = "#666666"
))

### |-  titles and caption ----
title_text <- str_glue("European Minimum Wages Rose 23-87% Since 2020, With Eastern Europe Leading Growth")

subtitle_text <- str_glue(
  "Growth trajectories and current positioning relative to €1,686 EU average, monthly statutory wages<br><br>",
  "Gray circles: 2020 starting wages | Colored dots/bars: 2025 final position (Blue: Above average, Red: Below average)"
)

# Create caption
caption_text <- create_mm_caption(
  mm_year = current_year,
  mm_week = current_week,
  source_text = paste0("Eurostat")
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    plot.title = element_text(
      size = rel(1.2), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0.5,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_text(
      size = rel(0.9), hjust = 0.5, family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9), lineheight = 0.9,
      margin = margin(t = 5, b = 20)
    ),

    # Legend formatting
    legend.position = "plot",
    legend.box.margin = margin(b = 10),
    legend.margin = margin(b = 5),
    legend.title = element_text(face = "bold"),

    # Axis formatting
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    axis.ticks.length = unit(0.2, "cm"),
    axis.title.x = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(t = 10)
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10)
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),
    axis.text.y = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),

    # Grid lines
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),
    panel.grid.minor = element_blank(),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code

```{r}
#| label: plot
#| warning: false

### |-  Plot 1  Dumbbell Chart (The Journey) ----
p1 <- dumbell_data |> 
  ggplot(aes(y = country)) +
  # Geoms
  geom_segment(aes(x = start_wage, xend = end_wage, 
                   color = above_avg), 
               linewidth = 1.1, alpha = 0.8) +
  geom_point(aes(x = start_wage), 
             color = "gray30", fill = "white",
             size = 3.5, shape = 21, stroke = 1.5) +
  geom_point(aes(x = end_wage, color = above_avg), 
             size = 4, alpha = 0.9) +
  geom_text(aes(x = end_wage,
                label = paste0("+", round(pct_change, 1), "%")),
            hjust = -0.35, size = 3.2, fontface = "bold", 
            color = colors$palette$text_dark) +
  # Scales
  scale_color_manual(
    values = c("TRUE" = colors$palette$above_avg, 
               "FALSE" = colors$palette$below_avg),
    guide = "none"
  ) +
  scale_x_continuous(
    labels = format_euro,
    expand = expansion(mult = c(0.1, 0.25)),
    limits = c(500, 2800),
    breaks = seq(500, 2500, 500)
  ) +
  # Labs
  labs(
    title = "A. Growth Journey (2020 --> 2025)",
    subtitle = "How countries transformed their minimum wages",
    x = "Monthly Minimum Wage",
    y = NULL
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.3),
      family = fonts$title,
      face = "bold",
      hjust = 0.5,
      color = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(1.1),
      hjust = 0.5,
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9),
      lineheight = 0.9,
      margin = margin(t = 5, b = 10)
    ),
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
  )

### |-  Plot 2 Diverging Bar Chart (Current Position) ----
p2 <- diverging_chart_data |> 
  ggplot(aes(x = country, y = diff_from_avg, fill = above_avg)) +
  # Geoms
  geom_col(width = 0.75, alpha = 0.85) +
  geom_hline(yintercept = 0, color = colors$palette$text_dark, 
             size = 1, alpha = 0.8) +
  geom_text(
    aes(label = format_euro(abs(diff_from_avg)),
        hjust = ifelse(diff_from_avg > 0, -0.15, 1.15)),
    size = 3.2, fontface = "bold", 
    color = colors$palette$text_dark
  ) +
  # Scales
  scale_fill_manual(
    values = c("TRUE" = colors$palette$above_avg, 
               "FALSE" = colors$palette$below_avg),
    labels = c("Below Average", "Above Average"),
    name = NULL
  ) +
  scale_y_continuous(
    labels = format_euro,
    breaks = scales::pretty_breaks(n = 6),
    limits = c(-1100, 1200)
  ) +
  coord_flip() +
  # Labs
  labs(
    title = "B. Current Position vs European Average",
    subtitle = paste0("Deviation from ", format_euro(round(current_avg)), " average"),
    x = NULL,
    y = "Difference from Average"
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.3),
      family = fonts$title,
      face = "bold",
      hjust = 0.5,
      color = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(1.1),
      hjust = 0.5,
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9),
      lineheight = 0.9,
      margin = margin(t = 5, b = 10)
    ),
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
  )

### |-  combined plot ----
# Create an invisible spacer plot
spacer <- ggplot() + 
  theme_void() +
  theme(plot.margin = margin(0, 0, 0, 0))

combined_plots <- (p1 + spacer + p2) +
  plot_layout(
    widths = c(1.2, 0.01, 1)
  )

combined_plots <- combined_plots +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(1.6),
        family = fonts$title,
        face = "bold",
        hjust = 0.5,
        color = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_markdown(
        size = rel(1.1),
        hjust = 0.5,
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.9),
        lineheight = 0.9,
        margin = margin(t = 5, b = 0)
      ),
      plot.caption = element_markdown(
        size = rel(0.75),
        family = fonts$caption,
        color = colors$caption,
        hjust = 0.5,
        margin = margin(t = 10)
      )
    )
  )
```

7. Save

Show code

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 12, 
  height = 8
  )
```

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/La_Paz
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      patchwork_1.3.0 glue_1.8.0      scales_1.3.0   
 [5] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[13] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[17] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0        generics_0.1.3    
 [9] curl_6.0.0         parallel_4.4.0     gifski_1.32.0-1    fansi_1.0.6       
[13] pkgconfig_2.0.3    ggplotify_0.1.2    skimr_2.1.5        lifecycle_1.0.4   
[17] compiler_4.4.0     farver_2.1.2       munsell_0.5.1      repr_1.1.7        
[21] janitor_2.2.0      codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1 
[25] yaml_2.3.10        crayon_1.5.3       pillar_1.9.0       camcorder_0.1.0   
[29] magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37     
[33] stringi_1.8.4      rsvg_2.6.1         rprojroot_2.0.4    fastmap_1.2.0     
[37] grid_4.4.0         colorspace_2.1-1   cli_3.6.4          magrittr_2.0.3    
[41] base64enc_0.1-3    utf8_1.2.4         withr_3.0.2        bit64_4.5.2       
[45] timechange_0.3.0   rmarkdown_2.29     bit_4.5.0          hms_1.1.3         
[49] evaluate_1.0.1     knitr_1.49         markdown_1.13      gridGraphics_0.5-1
[53] rlang_1.1.6        gridtext_0.1.5     Rcpp_1.0.13-1      xml2_1.3.6        
[57] renv_1.0.3         svglite_2.1.3      rstudioapi_0.17.1  vroom_1.6.5       
[61] jsonlite_1.8.9     R6_2.5.1           fs_1.6.5           systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2025_31.qmd.

For the full repository, click here.

10. References

Expand for References

Data:

Makeover Monday 2025 Week 31: Low wages across Europe

Article

Low wages across Europe

--- title: "European Minimum Wages Rose 23-87% Since 2020, With Eastern Europe Leading Growth" subtitle: "Growth trajectories and current positioning relative to €1,686 EU average, monthly statutory wages" description: "Analysis of European minimum wage changes 2020-2025 using improved dual-panel visualization. Eastern European countries led growth (Poland: 87%, Czechia: 51%) while Western nations maintain positions above €1,686 EU average." author: "Steven Ponce" date: "2025-07-29" categories: ["MakeoverMonday", "Data Visualization", "R Programming", "2025"] tags: [ "minimum-wage", "european-union", "economic-policy", "wage-growth", "data-transformation", "ggplot2", "dumbbell-chart", "diverging-bars", "comparative-analysis", "policy-visualization", "eastern-europe", "western-europe", "economic-inequality", "makeover-monday-2025", "dual-panel-charts" ] image: "thumbnails/mm_2025_31.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" self-contained: true theme: light: [flatly, assets/styling/custom_styles.scss] dark: [darkly, assets/styling/custom_styles_dark.scss] editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true --- ```{r} #| label: setup-links #| include: false # CENTRALIZED LINK MANAGEMENT ## Project-specific info current_year <- 2025 current_week <- 31 project_file <- "mm_2025_31.qmd" project_image <- "mm_2025_31.png" ## Data Sources data_main <- "https://data.world/makeovermonday/2025-week-31-wages-across-europe" data_secondary <- "https://www.datawrapper.de/blog/low-wages-europe" ## Repository Links repo_main <- "https://github.com/poncest/personal-website/" repo_file <- paste0("https://github.com/poncest/personal-website/blob/master/data_visualizations/MakeoverMonday/", current_year, "/", project_file) ## External Resources/Images chart_original <- "https://raw.githubusercontent.com/poncest/MakeoverMonday/refs/heads/master/2025/Week_31/original_chart.png" ## Organization/Platform Links org_primary <- "https://ec.europa.eu/eurostat/databrowser/explore/all/popul?lang=en&subtheme=labour.earn.earn_minw&display=list&sort=category&extractionId=earn_mw_cur" org_secondary <- "https://data.world/makeovermonday" # Helper function to create markdown links create_link <- function(text, url) { paste0("[", text, "](", url, ")") } # Helper function for citation-style links create_citation_link <- function(text, url, title = NULL) { if (is.null(title)) { paste0("[", text, "](", url, ")") } else { paste0("[", text, "](", url, ' "', title, '")') } } ``` ### Original The original visualization **Low wages across Europe** comes from `r create_link("Datawrapper", data_secondary)`  ![Original visualization](https://raw.githubusercontent.com/poncest/MakeoverMonday/refs/heads/master/2025/Week_31/original_chart.png) ### Makeover ![Dual-panel chart showing European minimum wage changes 2020-2025. Left panel: dumbbell chart displaying growth percentages from 23% (Luxembourg) to 87% (Poland), with gray circles marking 2020 starting wages and colored dots showing 2025 endpoints. Right panel: horizontal bar chart showing current deviation from €1,686 EU average, with blue bars for countries above average (Luxembourg highest at +€952) and red bars for countries below average (Czechia lowest at -€860). Eastern European countries exhibit the highest growth rates but remain below the EU average.](`r project_image`){#fig-1} ### <mark> **Steps to Create this Graphic** </mark> #### 1. Load Packages & Setup ```{r} #| label: load #| warning: false #| message: false #| results: "hide" ## 1. LOAD PACKAGES & SETUP ---- suppressPackageStartupMessages({ if (!require("pacman")) install.packages("pacman") pacman::p_load( tidyverse, # Easily Install and Load the 'Tidyverse' ggtext, # Improved Text Rendering Support for 'ggplot2' showtext, # Using Fonts More Easily in R Graphs scales, # Scale Functions for Visualization glue, # Interpreted String Literals patchwork # The Composer of Plots ) }) ### |- figure size ---- camcorder::gg_record( dir = here::here("temp_plots"), device = "png", width = 12, height = 10, units = "in", dpi = 320 ) # Source utility functions suppressMessages(source(here::here("R/utils/fonts.R"))) source(here::here("R/utils/social_icons.R")) source(here::here("R/utils/image_utils.R")) source(here::here("R/themes/base_theme.R")) ``` #### 2. Read in the Data ```{r} #| label: read #| include: true #| eval: true #| warning: false #| eu_wages <- read_csv( here::here('data/MakeoverMonday/2025/wages.csv')) |> janitor::clean_names() ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(eu_wages) skimr::skim(eu_wages) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false # Custom euro formatting function format_euro <- function(x) { paste0("€", scales::comma(x, accuracy = 1)) } # Data tidying eu_wages_long <- eu_wages |> mutate(date = dmy(date)) |> pivot_longer( cols = -date, names_to = "country", values_to = "wage" ) |> mutate( country = str_to_title(country), year = year(date), wage_euros = wage, wage_thousands = wage / 1000 ) |> arrange(country, date) # Calculate additional metrics eu_wages_summary <- eu_wages_long |> group_by(country) |> summarise( start_wage = first(wage_euros), end_wage = last(wage_euros), total_change = end_wage - start_wage, pct_change = (end_wage - start_wage) / start_wage * 100, avg_wage = mean(wage_euros), .groups = "drop" ) |> arrange(desc(end_wage)) # Calculate average current_avg <- mean(eu_wages_summary$end_wage) # Dumbell data dumbell_data <- eu_wages_summary |> mutate( country = fct_reorder(country, end_wage), above_avg = end_wage > current_avg ) # Diverging data diverging_chart_data <- eu_wages_summary |> mutate( diff_from_avg = end_wage - current_avg, country = fct_reorder(country, diff_from_avg), above_avg = diff_from_avg > 0 ) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- # Get base colors with custom palette colors <- get_theme_colors(palette = list( above_avg = "#1f77b4", below_avg = "#d62728", start_point = "#ff7f0e", end_point = "#2ca02c", connection = "#7f7f7f", text_dark = "#2f2f2f", text_light = "#666666" )) ### |- titles and caption ---- title_text <- str_glue("European Minimum Wages Rose 23-87% Since 2020, With Eastern Europe Leading Growth") subtitle_text <- str_glue( "Growth trajectories and current positioning relative to €1,686 EU average, monthly statutory wages<br><br>", "Gray circles: 2020 starting wages | Colored dots/bars: 2025 final position (Blue: Above average, Red: Below average)" ) # Create caption caption_text <- create_mm_caption( mm_year = current_year, mm_week = current_week, source_text = paste0("Eurostat") ) ### |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- # Start with base theme base_theme <- create_base_theme(colors) # Add weekly-specific theme elements weekly_theme <- extend_weekly_theme( base_theme, theme( plot.title = element_text( size = rel(1.2), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, hjust = 0.5, margin = margin(t = 5, b = 10) ), plot.subtitle = element_text( size = rel(0.9), hjust = 0.5, family = fonts$subtitle, color = alpha(colors$subtitle, 0.9), lineheight = 0.9, margin = margin(t = 5, b = 20) ), # Legend formatting legend.position = "plot", legend.box.margin = margin(b = 10), legend.margin = margin(b = 5), legend.title = element_text(face = "bold"), # Axis formatting axis.ticks.y = element_blank(), axis.ticks.x = element_line(color = "gray", linewidth = 0.5), axis.ticks.length = unit(0.2, "cm"), axis.title.x = element_text( face = "bold", size = rel(0.85), margin = margin(t = 10) ), axis.title.y = element_text( face = "bold", size = rel(0.85), margin = margin(r = 10) ), axis.text.x = element_text( size = rel(0.85), family = fonts$subtitle, color = colors$text ), axis.text.y = element_text( size = rel(0.85), family = fonts$subtitle, color = colors$text ), # Grid lines panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4), panel.grid.minor = element_blank(), # Margin plot.margin = margin(20, 20, 20, 20) ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot 1 Dumbbell Chart (The Journey) ---- p1 <- dumbell_data |> ggplot(aes(y = country)) + # Geoms geom_segment(aes(x = start_wage, xend = end_wage, color = above_avg), linewidth = 1.1, alpha = 0.8) + geom_point(aes(x = start_wage), color = "gray30", fill = "white", size = 3.5, shape = 21, stroke = 1.5) + geom_point(aes(x = end_wage, color = above_avg), size = 4, alpha = 0.9) + geom_text(aes(x = end_wage, label = paste0("+", round(pct_change, 1), "%")), hjust = -0.35, size = 3.2, fontface = "bold", color = colors$palette$text_dark) + # Scales scale_color_manual( values = c("TRUE" = colors$palette$above_avg, "FALSE" = colors$palette$below_avg), guide = "none" ) + scale_x_continuous( labels = format_euro, expand = expansion(mult = c(0.1, 0.25)), limits = c(500, 2800), breaks = seq(500, 2500, 500) ) + # Labs labs( title = "A. Growth Journey (2020 --> 2025)", subtitle = "How countries transformed their minimum wages", x = "Monthly Minimum Wage", y = NULL ) + # Theme theme( plot.title = element_text( size = rel(1.3), family = fonts$title, face = "bold", hjust = 0.5, color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_markdown( size = rel(1.1), hjust = 0.5, family = fonts$subtitle, color = alpha(colors$subtitle, 0.9), lineheight = 0.9, margin = margin(t = 5, b = 10) ), panel.grid.major.y = element_blank(), panel.grid.minor = element_blank(), ) ### |- Plot 2 Diverging Bar Chart (Current Position) ---- p2 <- diverging_chart_data |> ggplot(aes(x = country, y = diff_from_avg, fill = above_avg)) + # Geoms geom_col(width = 0.75, alpha = 0.85) + geom_hline(yintercept = 0, color = colors$palette$text_dark, size = 1, alpha = 0.8) + geom_text( aes(label = format_euro(abs(diff_from_avg)), hjust = ifelse(diff_from_avg > 0, -0.15, 1.15)), size = 3.2, fontface = "bold", color = colors$palette$text_dark ) + # Scales scale_fill_manual( values = c("TRUE" = colors$palette$above_avg, "FALSE" = colors$palette$below_avg), labels = c("Below Average", "Above Average"), name = NULL ) + scale_y_continuous( labels = format_euro, breaks = scales::pretty_breaks(n = 6), limits = c(-1100, 1200) ) + coord_flip() + # Labs labs( title = "B. Current Position vs European Average", subtitle = paste0("Deviation from ", format_euro(round(current_avg)), " average"), x = NULL, y = "Difference from Average" ) + # Theme theme( plot.title = element_text( size = rel(1.3), family = fonts$title, face = "bold", hjust = 0.5, color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_markdown( size = rel(1.1), hjust = 0.5, family = fonts$subtitle, color = alpha(colors$subtitle, 0.9), lineheight = 0.9, margin = margin(t = 5, b = 10) ), panel.grid.major.y = element_blank(), panel.grid.minor = element_blank(), ) ### |- combined plot ---- # Create an invisible spacer plot spacer <- ggplot() + theme_void() + theme(plot.margin = margin(0, 0, 0, 0)) combined_plots <- (p1 + spacer + p2) + plot_layout( widths = c(1.2, 0.01, 1) ) combined_plots <- combined_plots + plot_annotation( title = title_text, subtitle = subtitle_text, caption = caption_text, theme = theme( plot.title = element_text( size = rel(1.6), family = fonts$title, face = "bold", hjust = 0.5, color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_markdown( size = rel(1.1), hjust = 0.5, family = fonts$subtitle, color = alpha(colors$subtitle, 0.9), lineheight = 0.9, margin = margin(t = 5, b = 0) ), plot.caption = element_markdown( size = rel(0.75), family = fonts$caption, color = colors$caption, hjust = 0.5, margin = margin(t = 10) ) ) ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot_patchwork( plot = combined_plots, type = "makeovermonday", year = current_year, week = current_week, width = 12, height = 8 ) ``` #### 8. Session Info ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### 9. GitHub Repository ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo The complete code for this analysis is available in `r create_link(project_file, repo_file)`. For the full repository, `r create_link("click here", repo_main)`. ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References 1. Data: - Makeover Monday `r current_year` Week `r current_week`: `r create_link("Low wages across Europe", data_main)` 2. Article - `r create_link("Low wages across Europe", data_secondary)` :::